Core Analysis Results Research Articles

Enhancing permeability estimation methods in Brazilian pre-salt complex carbonate reservoirs

Carbonate pre-salt reservoirs pose formidable challenges for formation evaluation and estimation of petrophysical properties such as rock flow capacity. The high complexity of the pore system and its textural heterogeneity often lead to poor predictions when using conventional formation evaluation methods. This paper discusses and tests the applicability of two approaches to calculate the absolute permeability curves for the carbonate reservoirs of the Barra Velha Formation in the Santos Basin, proposing a fine-tuned workflow for these complex rocks. In developing our workflow, we used data from a case study well, comprising a complete well log suite and two formation tests, and routine core analysis results (RCAL) in core plugs and sidewall cores. Our workflow consists of: (1) reinterpreting the nuclear magnetic resonance (NMR) log in vugular intervals by decomposing the transversal relaxation time spectra; (2) calculating the absolute permeability by the Timur–Coates model and adjusting the coefficients with the RCAL results from representative samples; (3) applying the Flow Zone Indicator (FZI) method for rock typing to optimally define Hydraulic Flow Units (HFU); (4) checking the consistency of HFUs regarding pore geometry and lithological characteristics; (5) evaluating the performance of the models against core data and formation tests. The predicted permeability curves performed well for the NMR and FZI models when compared to the RCAL data from representative samples, with mean absolute errors for the logarithm of permeabilities equal to 0.78 and 0.87, respectively. This represents an improvement of 15% and 5% when compared to the simplistic approach that uses a power law to relate porosity to permeability from RCAL. These curves also reasonably reproduce the results from the formation tests for the top part of the reservoir, where matrix porosity dominates the flow. However, the NMR and FZI permeability curves underestimate the test results at the bottom part of the reservoir, where karstic and fracture features dominate, demonstrating limitations in these permeability estimation methods for this specific scenario. From our observations, we propose several improvements to enhance the current methods for estimating absolute permeability in pre-salt carbonate reservoirs. In the case of the NMR approach, it is essential to consider the effects of mud invasion in vugs for the correct estimation of the proportions of free and irreducible fluids. In the FZI approach, optimization in defining the quantity and limits of hydraulic flow units is important, which shows that four units are sufficient to represent the reservoir interval.

Reservoir rock typing for optimum permeability prediction of Nubia formation in October Field, Gulf of Suez, Egypt

Permeability prediction and distribution is very critical for reservoir modeling process. The conventional method for obtaining permeability data is from cores, which is a very costly method. Therefore, it is usual to pay attention to logs for calculating permeability where it has massive limitations regarding this step. The aim of this study is to use unique artificial intelligence (AI) algorithms to tackle this challenge and predict permeability in the studied wells using conventional logs and routine core analysis results of the core plugs as an input to predict the permeability in non-cored intervals using extreme gradient boosting algorithm (XGB). This led to promising results as per the R2 correlation coefficient. The R2 correlation coefficient between the predicted and actual permeability was 0.73 when using the porosity measured from core plugs and 0.51 when using the porosity calculated from logs. This study presents the use of machine-learning extreme gradient boosting algorithm in permeability prediction. To our knowledge, this algorithm has not been used in this formation and field before. In addition, the machine-learning model established is uniquely simple and convenient as only four commonly available logs are required as inputs, it even provides reliable results even if one of the required logs for input is synthesized due to its unavailability.

Semi-supervised interlayer intelligent recognition method

The development and distribution of interlayers in sandstones can lead to increased heterogeneity in the formation within the reservoir, which further affects the movement of fluids in the formation. Therefore, it is essential to accurately evaluate the interlayers in sandstones, as the precise evaluation of interlayers in sandstones is of great importance for identifying the distribution of underground fluid systems. The logging data of interlayers are inadequate for traditional Machine Learning training due to their low measurement proportion compared to the conventional layers. In logging data, the amount of data in interlayers is significantly smaller than that of conventional reservoirs. Traditional machine learning models are mostly based on samples with balanced distribution. By contrast, semi-supervised learning requires a small number of labeled samples for learning, and then combines a large number of unlabeled samples for modeling. To verify the feasibility of semi-supervised learning in the identification of interlayers, the Donghe sandstone section of the H oilfield was taken as an example. First, the core analysis results were used to label the logging data; then, to uncover more response information that can characterize the interlayers on the logging curve, multiple features were extracted to construct cross features. Finally, an improved model based on autoencoders—probabilistic autoencoder (PAE)—is proposed to address the issue of interlayer recognition for imbalanced samples. The PAE model can calculate the probability of belonging to a different class for unlabeled samples, and classify new samples according to the maximum probability. Experimental results show that, compared with traditional machine learning methods and ensemble learning methods, PAE achieves higher recognition accuracy and better generalization performance by updating the algorithm, and can be used as a simple and fast method for interlayer recognition. The results of the algorithm demonstrate that the semi-supervised method is of great significance for exploring and developing complex heterogeneous oil reservoirs. The research results are of great importance for exploring and developing complex heterogeneous oil reservoirs.

Using Digital Rock Physics to Evaluate Novel Percussion Core Quality

This paper details a benchmarking and validation workflow using digital rock physics (DRP) to evaluate the effectiveness of various percussion sidewall core (PSWC) acquisition methods. The workflow consists of obtaining digital rock properties and images of reference material to compare with material obtained from laboratory percussion sidewall acquisition, including novel designs. This workflow allows insight into the acquisition processes and potential sources of damage in the PSWC technique, and potentially other rock sampling techniques, and offers an opportunity to evaluate its appropriateness as a subsurface rock sample acquisition method. Sample cubes from six outcrop sandstone formations of known properties were used in the testing program to cover a wide range of particularly low and medium unconfined compressive strengths (UCS). Multiple control rotary plug samples were cut from each sandstone formation. The sample cubes were then used as the parent material in laboratory testing of various designs of PSWC bullets. The PSWC bullets, including novel designs, were shot in simulated downhole environments. Both control rotary plug samples and PSWC test core samples were imaged with high-resolution X-ray micro-computed tomography (micro-CT) at resolutions between 2 and 11 μm, and digital rock analysis was conducted on all samples. Using pre- and post-test images, the damage could be identified, and petrophysical properties, including porosity and permeability, could be determined and directly compared with DRP results from control samples and available routine core analysis (RCA) results. DRP provides unique insight to evaluate and quantify changes (or lack of changes) to the sample material subjected to the PSWC acquisition. Damage encountered in the test samples includes grain crushing and compaction that degrades storage and transport properties and dilatant zones that locally enhance transport properties. The presence, frequency, and distribution of these zones are dependent on experimental parameters. In all cases, undisturbed rock fabric could be identified in each sample, and intact texture was verified by comparison with reference material. A novel and efficient method for acquiring and evaluating subsurface samples was developed and benchmarked. Lab results indicate this method may be equally applicable to low- and mid-range UCS rocks. This approach enables a cost-effective reservoir characterization strategy. By optimizing PSWC bullet design and coupling this with mature, image-based digital rock technology, this work demonstrated that the samples and results obtained by this method are representative and that the controls on storage and transport properties are well understood.

Correction Method for Logging Curves in Clay-Rich Tight Glutenite Reservoir: Upper Wuerhe Formation in Mahu Oilfield, China

Mahu Oilfield is the largest tight glutenite oilfield in the world, and the upper Wuerhe formation is an important succeeding exploration horizon. However, the upper Wuerhe formation in the Mahu 1 zone has a high clay content, which can lead to serious wellbore collapse. Meanwhile, the horizontal well logging is not corrected. These factors lead to the inconsistency between the logging interpretation results and the oil test results. The interpretation precision of the clay content, water saturation, and porosity, which are crucial to reservoir evaluation, is very low. In this paper, a workflow of logging curve correction using multiple methods is proposed. The multiple linear fitting is used to correct boreholes, and then histogram frequency distribution matching is used to standardize multi-well logging curves. Finally, the optimization method is used to build a volume model based on skeleton analysis, and the results are calibrated with core analysis results. Horizontal well density logs are corrected using adjacent vertical well logs. The interpretation results of clay content, water saturation, and porosity with high precision are obtained. The reservoir interpretation is more in line with the oil test results than the original interpretation. The clay content distribution is more reasonable.

Improved triple porosity model for calculating porosity exponent of fractured-vuggy reservoirs based on Maxwell-Garnett mixing rule and anisotropic conductivity analysis

Porosity exponent (m) is a key parameter for calculating reservoir oil and gas saturation and evaluating reservoir effectiveness; the development of matrix pores, vug pores, and fractures makes the m response regularity of fractured-vuggy reservoirs unclear, which makes the accurate calculation of saturation difficult. By applying Maxwell-Garnett mixing rule to vuggy reservoirs and conductivity tensor analysis to fractured reservoirs, an improved Triple Porosity Model considering fracture dip angle is established. The newly improved Triple Porosity Model clarifies the response regularity of m to the matrix pores, vug pores, fracture pores, and fracture angles. Based on the improved Triple Porosity Model, the response regularity chart of m is further established. The findings show that: (1) m increases with the increase of fracture dip angle (θ) and the development of vugs; (2) m increases with the development of high-angle fractures and decreases with the development of low-angle fractures; (3) in fractured reservoirs, the fracture dip angle of 33° is the critical dip angle that determines the relationship between m and matrix porosity exponent (mb); (4) an increase in matrix porosity will weaken the influence of vugs, fractures, and fracture dip angle on m, which will eventually make m converge to mb. The field application of fractured-vuggy reservoirs shows that the water saturation calculated by the improved Triple Porosity Model is better in agreement with the core analysis results.

Predicting mineralogy by integrating core and well log data using a deep neural network

Mineralogy is an essential parameter for calculating the rock properties of reservoir formations. To evaluate mineralogy, mineralogy logs and core analysis results are generally used. Additional logs can provide information on mineralogy at all depth intervals of the borehole, but the provided information is acquired from indirect measurement with increasing costs. Core data can give direct measurements, but it is often uneconomical to acquire cores for all depth intervals. A deep neural network (DNN) can be used to extract relationships among multiple nonlinear features. Due to this advantage, DNNs have been actively applied to geophysical problems, which are solved through nonlinear equations. In this study, we propose a DNN model to predict the weight fractions of minerals from integrated conventional log data and X-ray diffraction (XRD) results analyzed using core data. To compensate for the limited XRD results, Fancy principal component analysis was adopted to augment the XRD results before training the DNN. We conducted blind tests to verify the effectiveness of the trained DNN model using field data. The trained DNN model is reliable and cost effective, demonstrating applicability to the prediction of mineralogy.

A novel approach to total organic carbon content prediction in shale gas reservoirs with well logs data, Tonghua Basin, China

As a geochemical parameter for characterizing source rock in shale gas reservoirs, estimation of total organic carbon (TOC) is a main task of geophysical and geochemical studies. TOC can be used to evaluate the hydrocarbon generation potential of source rocks. Artificial intelligence (AI) methods have been proposed recently to obtain TOC from well logs. This avoids expensive and time-consuming core analysis of geochemical experiments. However, the optimal combination (selected) of logs should be determined to get the highest accuracy TOC. We used a well-trained least square support vector machine model to select appropriate well log inputs for intelligent model based on mean impact values. We used a conventional 9 logs obtained from a shale gas well in Tonghua Basin, China, to test our method. The results were compared with 215 TOC values from core analysis. In addition, we tested three AI of models, including artificial neural network based on backpropagation algorithm (ANN-BP), least square support vector machine (LSSVM), and particle swarm optimization-least square support vector machine (PSO-LSSVM). For these three models, both selected logs and all logs are used for comparison. The TOC results, obtained from different calculations, showed that selected logs significantly improved the TOC accuracy in each AI model. By comparing different AI model, we found that PSO-LSSVM model outperforms the other two models. The TOC obtained from the PSO-LSSVM can benchmark with core analysis results.

Reservoir characteristics and well-logging evaluation of the Lower Cambrian shales in southeast Chongqing, China

AbstractTo accurately identify the reservoir characteristics and main controlling factors of the Cambrian Niutitang black shale in the southeast Chongqing, a series of systematic measurements were conducted on core samples from Well Yuke-1 and Well Youke-1. The measurements include clay mineral analysis, XRD analysis, petrophysical properties, specific surface area, pore diameter and TOC, as well as Ro and maceral compositions. The Niutitang shale reservoir contains detrital minerals and clay minerals averagely of 51.34% and 32.74%, respectively. The average effective porosity and permeability are 1.2% and 8.0×10-3mD, respectively, typically of ultra-low porosity and permeability. Mesopores are dominant, accounting for approximately 73% of the total pore volume. The average BET specific surface area is 7.75m2/g and the average pore diameter is 5.3nm. The average TOC is 2.29% and the average Ro is 3.12%, indicating that the organic matter is in over-maturity stage. Statistical analyses of the measurements show that the micropore volume is positively correlated with TOC but negatively correlated with the total content of clay minerals; the mesopore volume is positively correlated with TOC, the total content of clay minerals and the content of carbonate minerals; the macropore volume is positively correlated with TOC and the content of carbonate minerals, weakly positively correlated with the content of detrital minerals but negatively correlated with the total content of clay minerals. The porosity and TOC of the Niutitang shale are computed using the conventional well-logging method for the Yuke-1 and Youke-1 wells, and the porosities are quite consistent with the core analysis resultsr, but in the shallow reservoir that contains no oil and gas, the TOC exhibits no correlation with the core analysis results.

Estimation of water saturation by using radial based function artificial neural network in carbonate reservoir: A case study in Sarvak formation

Water saturation determination in core laboratory is known as a cost and time consuming labor. Hitherto, many scientists attempted to estimate accurately water saturation from well-logging data which has a continuous record without losing information. Therefore, various model were introduced to relate reservoir properties and water saturation. Since carbonate reservoir is very heterogeneous in shape and size of pore throat, the relation between water saturation and other carbonates reservoir properties is very complex, and causes considerable overall errors in water saturation calculation. By increasing the usage and improvement of soft computing methods in engineering problems, petroleum engineers have been attended them to measure the petrophysical properties of the reservoir. In this study, a radial basis function neural network (RBFNN) improved by genetic algorithm has been employed to estimate formation water saturation by using conventional well-logging data. The used logging and core data have been gathered from a carbonated formation from one of oilfield located in south-west Iran, and finally their results of the proposed model were compared with the core analysis results. By checking the testing data from another well, it showed this method had a 0.027 for mean square errors and its correlation coefficient is equal to 0.870. These results implied on high accuracy of this model for oil saturation degree estimation. While the common methods like Archie, had a 0.041 mean square error and 0.720 of the correlation coefficient, which indicate a high ability of RBF model than the other usual empirical methods.

Estimation of shear wave velocity from wireline logs in gas-bearing shale

Abstract Gas-bearing shale is one of the most important unconventional reservoirs. Shear wave velocities are essential for prestack AVO analysis, fracture identification, and fluid typing. Moreover, the recent studies of shear wave prediction mostly focused on conventional sandstones, wet shale, sandstone or carbonate rock may not be suitable for gas-bearing shale. Firstly, according to core analysis results of gas-bearing shale, petrophysics model is proposed. Then, shear wave prediction method based on Gasmann's theory, spatial averaging model, and elastic moduli of dry rock were studied in detail. Especially, point to organic shale with low porosity, Krief, Nur, and Pride models are selected to calculate the elastic moduli of dry rock, respectively, and the best critical porosity in Nur model and consolidation coefficient in Pride model are determined, which is virtual to gas-bearing shale. Pride model is finally optimized as the most suitable model through comparison of error analysis. Meanwhile, to implement the estimation of shear wave from well logs, a corresponding log interpretation method for gas-bearing shale is also studied. In case study, calculated volumetric concentrations of minerals are in good agreement with X-ray diffraction (XRD) analysis of cores. The final shear wave velocity also matches well with dipole sonic imaging (DSI) log. Therefore, the whole approach is verified correct and suitable for estimation of shear velocity or slowness in gas-bearing shale.

LITHOFACIAL CHARACTERISTICS OF BAKHMUT DEPRESSION UPPER AND MIDDLE CARBONIFEROUS SEDIMENTS (ON THE EXAMPLE OF ARTEMOVSKAYA-1 WELL CORE ANALYSIS)

Bakhmut depression is located in the Dnieper graben and Donetsk structure fold junction zone and it is a complex negative structure. One of the perspective structures within the Eastern Part of the depression is Artemovskaya structure. Stratigraphic well №1 4500 m deep was drilled in it. Actually, this report contains the lithogenetic investigations of well core analysis results.

In Situ Viscosity of Heavy Oil: Core and Log Calibrations

Abstract Having knowledge of oil viscosity variation within reservoirs would be of considerable benefit when producing from heavy oil fields. Previous work has demonstrated that low field NMR bench-top instruments can be used to perform measurements of in situ viscosity. Ideally, if these measurements could be performed on NMR logging tools, viscosity characterization studies could be carried out using fewer core samples. In this paper, data is presented for a heavy oil reservoir in northern Alberta. A methodology is presented for tuning NMR viscosity estimates to the field in question, and core analysis results are collected, showing that in situ viscosity predictions are possible in the laboratory. NMR spectra measured in the laboratory are compared to NMR logging tool spectra, in order to determine if results obtained using bench-top instruments can be extrapolated to logging tool data. Introduction Canada has significant proven reserves from the oil sands in Saskatchewan and northern Alberta, which constitute some of the largest resource bases in the world. With the decline of conventional oil reserves in Canada, interest is shifting rapidly to the production of this heavy oil. Heavy oil and bitumen are characterized by high fluid viscosity and density values similar to that of water. The high oil viscosity is the single greatest impediment to the successful recovery of this resource, and the viscosity is directly related to both the technical success of any chosen recovery scheme and the economic value of the oil. As a result, oil viscosity information is key when estimating reserves and developing recovery options from heavy oil and bitumen formations. In many heavy oil and bitumen reservoirs in Alberta, and likely all over the world, viscosity may vary significantly with depth and location(1,2). This may be due to biodegradation of the bitumen(2), along with other factors such as supply of micro-organisms to the oil and the solution gas-oil ratio. A result of this viscosity variation is that any chosen thermal or non-thermal enhanced oil recovery option will vary in effectiveness in different parts of the reservoir. Therefore, being able to map viscosity variations, along with proper characterization of rock properties, will also significantly affect the economics and recovery from heavy oil projects. Viscosity is conventionally measured by two different methods(3). Samples are either taken from the produced fluid from the wellhead, or oil sand samples are taken into the laboratory and oil is extracted in order to measure its viscosity. The difficulty in making measurements on wellhead samples is that the oil may have been contaminated by diluents or drilling fluid(3), or may contain significant emulsified water from thermal operations. This means that viscosity values obtained from wellhead oil samples must be used carefully and should be analyzed in order to ensure that they are truly representative of the oil viscosity in the formation. Measurements on bitumen that has been extracted from core samples are generally more accurate, but also tend to be more expensive. Care must be taken to ensure that enough samples are taken to properly characterize the fluid viscosity in the reservoir.

Permeability Determination Using Neural Networks in the Ravva Field, Offshore India

Summary This paper describes the use of the backpropagation neural network (BPNN) technique to predict reservoir permeability using conventional well log data. The technique is demonstrated with an application to the Ravva oil and gas field, offshore India. The Ravva field reservoirs are middle Miocene age nearshore marine sandstones that are often laminated to thinly interbedded shale. The use of conventional permeability-porosity crossplots to predict permeability in this field was not successful. The BPNN permeability prediction model ("RAVVANET") was developed from a data set consisting of core permeability and well log data from two early development wells. The model was blind tested with data from a third well, which was withheld from the modeling process. The results of this study show that BPNN model permeability predictions are consistent with core analysis results.

BAYU NUCLEAR MAGNETIC RESONANCE STUDY: COMMERCIAL APPLICATION OF APPLIED RESEARCH AND FIELD TECHNIQUES IN A GAS-CONDENSATE FIELD

Nuclear Magnetic Resonance (NMR) technology was applied to assist reservoir characterisation of the gas-bearing sandstones in the Bayu-Undan Field, Zone of Cooperation, Area A Blocks 91–12 and 91–13. NMR logs were run in several wells and laboratory NMR measurements on water-saturated core plugs were used to determine the sandstone pore structure and characterise effective porosity and permeability in the gas-bearing sands.These sandstones exhibit a range of pore size behaviour, ranging from those sandstones with no clay-bound water to those with a significant proportion of their porosity associated with micropores. Mean relaxation times tend to be slower than expected for the observed pore sizes in these sands by comparison with global analogs. Surface relaxivity is lower than the world-wide average for sandstones, and reflects a network of unusually clean pore- walls.Comparison between core permeability values and the log-estimated permeabilities over a four-order of magnitude range produces a correlation coefficient of greater than 0.7 in a single well.Integration of field- and laboratory-based NMR data proved that permeability and pore-size distribution models could be integrated with conventional core analysis results. These datasets are continuous over the reservoir interval and once calibrated are easily integrated into reservoir simulation models.

Proper Coring and Wellsite Core Handling Procedures: The First Step Toward Reliable Core Analysis

E&P Exchange Coring and wellsite core handling should follow the best possible practices because the value of all core analysis is limited by this initial operation. The objectives of any coring and core preservation program should be to obtain rock that is representative of the formation while minimizing physical alteration of the rock during coring and handling. Core analysis is an important component of formation evaluation and is especially advantageous when complex reservoirs are to be developed (see Table 1). The major problems encountered during coring, handling, and preserving reservoir rocks are (1) designing a bottomhole coring assembly and drilling-fluid program to minimize mud invasion and maximize drilling parameters, (2) selecting a nonreactive core preservation material and method to prevent fluid loss or the absorption of contaminants (e.g., wettability altering drilling fluid components), and (3) applying appropriate core handling and preservation methods based on rock type, degree of consolidation, and fluid type. Different rock types may require additional precautions in obtaining representative core data. Double-tube core barrels have effectively replaced rubber-sleeve coring methods for unconsolidated and fractured rock. Disposable inner barrels made of fiberglass or aluminum work best because of their low friction coefficients and ability to prevent jamming. Full-closure core catcher systems have contributed to obtaining better core recoveries of intact unconsolidated rock. Coring technology has advanced significantly in the last 3 years to maximize recoveries in most rock types while minimizing core damage. Low-invasion coring equipment is widely available and has proved critical when advanced reservoir studies are to be conducted for petrophysical and reservoir engineering purposes (e.g., electrical properties, relative permeability testing, and capillary pressure measurements). There is no one best method for handling and preserving cores. Core preservation is an attempt to maintain core, before analysis, in the same condition that existed when it was removed from the core barrel. In the process of cutting, recovering, and bringing the core to the surface, the fluid content of the rock is altered by unavoidable changes in pressure and temperature; pressure-retained cores attempt to minimize this effect. Experience can help determine the most satisfactory preservation method for the rock type in question. The core preservation method should depend on the composition, degree of consolidation, and distinguishing features of the rock. The techniques required to preserve cores for testing may depend on the length of time for transportation, storage, and the nature of the test(s) to be performed. Rock types that require special procedures for coring and wellsite preservation are unconsolidated rocks to include those containing both heavy and light oil, vuggy carbonates, evaporites, fractured rock, rocks rich in clay minerals, shale, low-permeability rock (tight gas sand), coal, and diatomite. Preferred methods to preserve cores for laboratory analysis include mechanical stabilization; environmentally controlled preservation through chilling, freezing, and regulated humidity control (freezing is probably the most common and most controversial method for preserving unconsolidated rocks); storage in heat-sealable plastic laminates or plastic bags; use of dips and coatings; sealing in disposable inner core barrels; and containment in specialized sealed devices, such as anaerobic jars. Totally unconsolidated rock is best handled frozen because mechanical alteration is likely during handling and preparation before analysis. The full effect of freezing on a core's petrophysical properties is unknown; however, plug preparation of unfrozen unconsolidated rock is wrought with many difficulties (e.g., plunge cutting). Unconsolidated frozen core should be packed with a surface ice cake to reduce fluid evaporative losses by sublimation. Consolidated rocks should never be frozen because freezing may cause irreversible structural damage to the core. The fundamental objective of core analysis is to obtain data representative of in-situ reservoir rock properties. Coring, handling, and preservation should be conducted in a manner that prevents both loss of interstitial fluids and contamination with foreign fluids. To obtain reliable core analysis, speed is essential in removing, laying out, labeling, and preserving the core. Sampling of core material at the well site should be limited to plugging for invasion or wettability studies. Core should never be washed and must be protected from temperature extremes, moisture, and dehydration. A multidisciplinary team must be assembled to ensure representative core analysis results. This team should include drilling engineers, reservoir engineers, geoscientists, and other individuals involved with laboratory testing. Suitable acquisition and testing protocol must be adopted to obtain meaningful data. A properly designed program will benefit not only the near-term user but also future users of the core material. References Skopec, R.A.: "Recent Advances in Rock Characterization," The Log Analyst (1992) 33, No. 3, 270-85. Rathmell, J.J., Gremley, R.B., and Tibbitts, G.A.: "Field Applications of Low Invasion Coring," paper SPE 27045 presented at the 1993 SPE Annual Technical Conference and Exhibition, Houston, Oct. 3-6. " Wellsite Core Handling Procedures," API, Dallas (in preparation). P. 280^

Log-derived reservoir properties and porosity preservation of Upper Jurassic Arab Formation in Saudi Arabia

Various types of wireline log analysis were conducted for the Arab Reservoirs of Upper Jurassic age in eastern Saudi Arabia. After evaluating the values of the cementation factor m in the Archie equation and the formation water resistivity R w, values such as total, primary and secondary porosities, lithological composition, water (or hydrocarbon) saturation and permeability were estimated using combinations of various logs. Some of the results, such as the estimated porosity and permeability, were then compared with the available core analysis results. The estimated percentage of dolomite was also plotted against the percentage porosity in an attempt to show a possible effect of dolomitization on porosity; dolomitization seems to increase the porosity in the early and intermediate stages, but tends to reduce porosity in the later or near-completion stages of dolomitization. This finding coincides with the earlier work of Power based on petrographical and petrophysical studies. The calculated average porosity of the Arab Reservoirs is up to 30%, which is exceptionally high if the effects of depth of burial between 4000 and 9000 ft and the geological age of about 160 million years (Upper Jurassic) are taken into consideration. To explain such high porosity values for the Arab Reservoirs, probable geological, physical, chemical factors, such as acidic formation fluids, reduced fluid mobility and ion-exchange capacity, tectonic forces and dolomitization are suggested.

Interpretation of reservoir rock mechanical properties by direct methods

Direct core analysis results of bulk density, porosity permeability, resistivity, transit time, and strength were correlated with logging data from an oil well. The interpretations were made in terms of index values between field and laboratory data obtained for the two reservoir units studied. An engineering classification scheme was suggested. Other rock engineering properties such as sonic velocity and dynamic modulus were calculated as well. An attempt was made to predict the fracture pressure gradient (FPG) of rock formation. This work has revealed the possibility of using the direct measured data to evaluate some of the in situ rock behavior.

New Core Analysis Methods for Measuring Reservoir Rock Properties of Devonian Shale

Summary We developed a new method involving crushed core samples to measure porosityand fluid content accurately in the black, organic-rich Devonian shales in the Appalachian basin. In the four wells studied, average core bulk volume of gasand porosity ranged to 4% and 8%, respectively, values substantially higherthan previously reported. previously reported. Introduction The Devonian shale in the Appalachian basin is considered a major potentialgas source. Because of analytical difficulties, core potential gas source. Because of analytical difficulties, core analysis in shales for reservoirproperties is not done often. Previous reports 1-3 show typical core porositiesof 2% to 3% in Previous reports 1-3 show typical core porosities of 2% to 3% inthe Devonian shale and free-gas content less than 0.1 % to 0.2 % by bulkvolume. Estimates of gas in place associated with these low free-gas contentsare an order of magnitude lower than is customarily produced in Devonian shalewells with long-term production history. produced in Devonian shale wells withlong-term production history. The goal of this study, sponsored by the Gas Research Inst. (GRI), was to improve our understanding of formation evaluationand reservoir description in shales. Four wells have been air-drilled recentlyin Kentucky and West Virginia from which extensive formation evaluation data, including 519 ft of conventional cores in the Devonian shale, have beencollected (Fig. !). Core recovery by formation is shown for each well in Table1 (the wells were drilled in the sequence shown). These wells were designatedcomprehensive study wells (CSW's) because of the extensive GRI- fundedevaluation-data program. One purpose of the CSW program was to develop newformation evaluation methods to identify gas-producing intervals, for bothdeliverability and reserves, with logging tools. Significant progress has beenmade. A key element of these new is to progress has been made. A key element ofthese new is to establish the reservoir rock and fluid properties throughcareful core analysis and then to develop the log interpretation methodsnecessary to match core analysis and production results. In the four CSW'S, cores range from shaly siltstones and laminated siltstones and shales to siltyshales. Mean grain size ranges from 0.01 to 0.06 mm. Mineralogy and petrographyshow the shale to be predominantly quartz and illite, with other mineralsincluding pyrite and kerogen (which can range up to 30 vol %). In CSW No. 2, the first well cored in the CSW program, very little kerogen and oil werepresent. Core analyses were similar to those in other low-permeabilitygas-productive reservoirs: water content was determined by weight loss afterextended drying in a convection oven at 230F, which removes both free andclaybound water. We measured grain volume of the dry core with helium and bulkvolume by immersing the core in mercury. PV is the difference between bulk andgrain volume. Free-gas content is the difference between PV and water content. These measurements were made at 1-ft intervals from the cored interval. Becausethese cores, and those from CSW Nos. 4A, 5, and 1A, were drilled in air-mistand the cores were preserved at the wellsite, the fluid saturations in the coreshould represent those in the reservoir after appropriate correction forcompaction to reservoir stress. When core analyses were begun on the next well, CSW No. 4A, different methods were required because the cores containsignificant amounts of oil and kerogen. Initially, core pieces were extractedin boiling toluene with the Dean Stark method for up to 2 weeks; the cores weredried in a convection oven at 230F for about 2 weeks; and PV's were determinedwith helium. This resulted in grain densities and porosities that were much toolow compared with log analyses. A new method of core analysis was developedthat involves crushing the core to speed up the processes of extraction, drying, and grain volume measurement. This new method results in much higherporosity and gas content values in the shale samples from CSW No. 4A, whereboth methods were used. The following sections describe the new core analysismethods. Porosity results are shown by a comparison of the new methods to Porosity results are shown by a comparison of the new methods to conventionalanalyses in normal-permeability rocks and in cores from the kerogen-free CSWNo. 2 to verify the new methods. Porosity and fluid content are shown fromcompanion sets of 95 crushed samples run by two separate laboratories toillustrate repeatability. Core analysis results are compared with a loganalysis in one of the study wells. Core analysis results for all four studywells are summarized. Finally, examples of other applications of analyses ofthe companion crushed sample sets are shown. P. 1184

The Use of Routine and Special Core Analysis in Characterizing Brent Group Reservoirs, U.K. North Sea

This paper describes the use of core analysis data, both routine and special, in characterizing the Brent Group reservoirs in the U.K. North Sea. The results of various special core analysis tests conducted over the years indicate that coring fluid, core preservation, and laboratory procedures are important in defining relative permeability and capillary pressure. Examples are given of the effect of oil-based mud filtrate on rock wettability; the effect of extraction, drying, and test procedures on laboratory waterflood performance; and variation of relative permeability among facies. Results also suggest how petrography may be used in assigning relative permeabilities by facies. Analysis of routine core data shows complexity within the Brent Group reservoirs even within relatively uniform sands.